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Shaoguang Li University of Massachusetts Medical School, USA
Dongguang Li Edith Cowan University, Australia
World Scientific NEW JERSEY
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Published by World Scientific Publishing Co. Pte. Ltd. 5 Toh Tuck Link, Singapore 596224 USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601 UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE
British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library.
DNA MICROARRAY TECHNOLOGY AND DATA ANALYSIS IN CANCER RESEARCH Copyright © 2008 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher.
For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy is not required from the publisher.
ISBN-13 978-981-279-045-3 ISBN-10 981-279-045-4
Typeset by Stallion Press Email:
[email protected] Printed in Singapore.
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Preface
DNA microarray technology has become a useful technique in gene expression analysis for the development of new diagnostic tools, and for the identification of disease genes and therapeutic targets for human cancers. Appropriate control for DNA microarray experiment and reliable analysis of the array data are the key to performing the assay and utilizing the data correctly. The most difficult challenge has been the lack of a powerful method to analyze the data for all genes (more than 30000) simultaneously and to use the microarray data in a decision-making process. In this book, we attempt to describe DNA microarray technology and data analysis by pointing out current advantages and disadvantages of the technique and available analytical methods. An important part of the book is that we will include some new ideas and analytical methods based on our own experience in DNA microarray study and analysis. We believe that our new way of interpreting and analyzing the microarray data will bring us closer to success in decision making using the information obtained through DNA microarray technology.
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Introduction of Authors
Shaoguang Li, M.D., Ph.D. Division of Hematology/Oncology Department of Medicine University of Massachusetts Medical School Lazare Research Building #315 364 Plantation Street Worcester, MA 01605 USA Tel: +1 508 856 1691 Email:
[email protected] Dr. Shaoguang Li obtained his M.D. degree from China Medical University, Shenyang, China; and his Ph.D. degree from Tulane University, New Orleans, Louisiana, USA. He did his postdoctoral training at Harvard Medical School in Boston, Massachusetts, USA; and began his independent scientific career at The Jackson Laboratory, Bar Harbor, Maine, USA. He is currently an Associate Professor at the University of Massachusetts Medical School, Worcester, Massachusetts, USA. His research interests have been in understanding the molecular basis of leukemogenesis and in developing effective and curative therapeutic strategies. He has made significant contributions to the research field of Philadelphia chromosome-positive (Ph+) leukemia using a mouse leukemia model, including the discovery of the roles of Src family kinases and leukemia stem cells in leukemia initiation/progression and resistance to kinase inhibitors. One of his main research focuses is on understanding the biology of leukemia stem cells for developing curative anti-stem cell therapy for Ph+ leukemia and other types of cancer. vii
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DNA Microarray Technology and Data Analysis in Cancer Research
Dongguang Li, Ph.D. School of Computer and Information Science Edith Cowan University 2 Bradford Street Mount Lawley, WA 6050 Australia Tel: +61 8 93706358 Email:
[email protected] Dr. Dongguang Li is currently an Associate Professor in the School of Computer and Information Science at Edith Cowan University, Australia, and is a member of the Institute of Electrical and Electronics Engineers (IEEE) and the Optical Society of America (OSA). He obtained an M.Eng. degree in optical engineering from the University of Shanghai for Science and Technology, China, in 1984; and a Ph.D. in physics from the University of New South Wales, Australia, in 1994. He has had a productive research career spanning more than two decades. During this time, he has made important and significant contributions to a variety of scientific fields, in particular the broad fields of optical engineering, global optimization, and information technology. His research interests have been in global optimization algorithms and their applications in intelligent ballistics image processing and optical thin film design. He invented a unique and innovative true global optimization algorithm for optical thin film design. He is the author of the optical thin film design software, OpteFilm, and its upgraded version, OnlyFilm. He has also established a database system based on ballistics image processing for firearm identification, for which he was awarded the 2006 Australian B-HERT (Business–Higher Education Round Table) Award for best research and development collaboration. Recently, he has expanded his research interests to the field of microarray data mining.
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Contents
Preface
v
Introduction of Authors Chapter 1
Chapter 2
Chapter 3
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1.1. 1.2. 1.3. 1.4. 1.5.
2 4 6 8 8
Experimental Procedure Experimental Design Quality Control Interpretation of DNA Microarray Data Advantages and Disadvantages
Applications of DNA Microarray Technology in Cancer Research
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2.1 Solid Tumors 2.2 Blood Cancers 2.3. Our DNA Microarray Study Using Mouse Model of BCR-ABL-Induced Leukemia 2.3.1. Leukemia mouse model study 2.3.2. Cell line study
13 15 19
Current Analytical Methods of DNA Microarray Data
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3.1. Experimental Design 3.2. Method
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Chapter 4
Chapter 5
3.2.1. Robust multi-chip averaging (RMA) 3.2.2. iterPLIER 3.3 Quality Control Diagnostics 3.3.1. Saturation 3.3.2. Transformed intensities across arrays 3.3.3. Normalized intensities across arrays 3.3.4. Scatterplot of normalized intensities 3.3.5. Average MA plot of normalized intensities 3.4. Statistical Analysis 3.4.1. Analysis of variance (ANOVA) model 3.4.2. Contrasts 3.5. Coefficient of Variation Analysis
31 32 32 33 36
A Novel Method for DNA Microarray Data Analysis: SDL Global Optimization Method
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4.1. Research Subjects 4.2. Experimental Design 4.2.1. Rationale 4.3. Fold Change Analysis 4.4. More Information on SDL Global Optimization 4.4.1. Genetic algorithms (GAs) 4.4.2. SDL global optimization algorithms
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Applications of the SDL Global Optimization Method in DNA Microarray Data Analysis
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5.1. Leukemia Cell Line Study 5.1.1. Introduction 5.1.2. Datasets 5.1.3. Analysis strategies 5.1.4. Discussion and conclusion 5.2. Analyses of Publicly Available Human Microarray Data 5.2.1. Introduction 5.2.2. Datasets
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5.3. Overall Methodology 5.3.1. Orthogonal arrays (OAs) and sampling procedure 5.3.2. Objective function 5.3.3. Search space reduction for global search 5.3.4. Mathematical form of SDL optimization 5.3.5. Multi-subset class predictor 5.3.6. Validation (predicting through a voting mechanism) 5.4. Experimental Results 5.5. Discussion 5.6. Conclusion Chapter 6
General Discussion and Future Directions
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71 71 74 76 80 82 83 83 90 99 101
References
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Index
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CHAPTER 1
DNA Microarray Technology
All living organisms are composed of cells. As a functional unit, each cell can make copies of itself, and this process depends on a proper replication of the genetic material known as deoxyribonucleic acid (DNA). DNA contains genes, and each structural gene functions by transcribing it into the corresponding messenger RNA (mRNA) using DNA as a template and ultimately translating into the corresponding protein using mRNA as a template (Fig. 1.1). The abundance and stability of proteins determine the functions of a cell. Thus, the function or activity of a gene is reflected by synthesis of mRNA (transcription) or protein (translation). DNA microarray technology measures the activity of genes at a transcriptional level. DNA microarrays (sometimes called DNA chips) are in general characterized by a structured immobilization of DNA targets in the free nucleic acid samples on planar solid supports, on which different types of nucleic acids with known sequences (known as “probes”) are fixed. A probe may be derived from complementary DNA (cDNA), polymerase chain reaction (PCR) products, or synthetic oligomers. In general, applications of DNA microarray technology broadly include (1) gene expression analysis (transcription analysis), which analyzes the transcriptional activity of genes through hybridization between DNA targets and probes; (2) genotyping with oligonucleotide arrays, which is based on the notion of combining the complete sequence of a DNA sample by presenting all possible sequences as a complement on the chip (Drmanac et al., 2002); (3) measurement of enzyme activities on immobilized DNA, which is based on the finding that DNA-modifying enzymes are capable of acting on immobilized DNA templates or oligonucleotides (Bier et al., 1996a; Bier et al., 1996b; Buckle et al., 1996); (4) PCR on the chip, which was 1
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Fig. 1.1. Activation of signaling pathways at different levels in cells that are stimulated by growth factors. Binding of growth factors to the cell surface receptor leads to activation of intracellular downstream known (solid lines) and unknown (broken lines) signaling molecules (colored blocks). The end result of this activation is to turn on the transcription of genes and ultimately the translation of mRNA into proteins that promote or inhibit cell growth. Measurement of synthesis of either mRNA or protein will reflect the function of a gene.
first described in 2000 (Adessi et al., 2000); and (5) transcription on chip, which shows the transcription of a complete gene into mRNA on the chip (Steffen et al., 2005). In this book, we focus on gene expression analysis using DNA microarray technology. 1.1. Experimental Procedure The procedure of a DNA microarray experiment includes multiple steps from sample preparation to data analysis (Fig. 1.2), among which
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1. Sample preparation: - isolation of total RNA - reverse transcription and amplification - labeling
2. Hybridization: - binding between the targets and probes - washing
3. Detection: - chip reading
4. Data acquisition and analysis: - collection and summary of raw data - statistical analysis of the data
Fig. 1.2.
Workflow of a DNA microarray experiment.
hybridization is a central process. The sample that contains the targets to be investigated is added to the DNA chip to allow their binding to the probes fixed on the chip, resulting in a characteristic-binding pattern representing the levels of gene expression of the sample. The sample itself is labeled prior to the hybridization, and the most-often-used labels are fluorescence labels that allow detection of the binding event. After the hybridization, the chip is washed and then fluorescence intensities on the chip are read and recorded by a scanning or imaging device. Raw data reflecting the fluorescence intensities are statistically analyzed and often shown by fold changes as compared to control. In our laboratory, we performed DNA microarray experiments to study gene expression profiling in mouse leukemia cells. Here is an example of the procedures for carrying out the microarray experiments. Briefly, cells are dissolved in RNAlater (Ambion, Austin, TX, USA) and homogenized in RLT Buffer (RNeasy Micro Kit; Qiagen, Valencia, CA, USA). Total RNA is isolated by following the protocol for the RNeasy Micro Kit, and quality is assessed using a 2100 Bioanalyzer instrument and RNA 6000 Pico
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LabChip assay (Agilent Technologies, Palo Alto, CA, USA). Utilizing the GeneChip Whole Transcript Sense Target Labeling Assay kit (Affymetrix, Santa Clara, CA, USA), 100–300 ng of total RNA undergoes reverse transcription with random hexamers tagged with T7 sequence. The doublestranded cDNA that is generated is then amplified by T7 RNA polymerase to produce cRNA. Second-cycle first-strand cDNA synthesis then takes place, incorporating dUTP, which is later used as sites where fragmentation occurs by utilizing a uracil DNA glycosylase and apurinic/apyrimidinic endonuclease 1 enzyme mix. The fragmented cDNA is then labeled by terminal transferase, attaching a biotin molecule using Affymetrix proprietary DNA Labeling Reagent. Approximately 2.0 µg of fragmented and biotin-labeled cDNA is then hybridized onto a Mouse Gene ST 1.0 Array (Affymetrix, Santa Clara, CA, USA) for 16 hours at 45°C. Posthybridization staining and washing are performed according to the manufacturer’s protocols using the Fluidics Station 450 instrument (Affymetrix). Finally, the arrays are scanned with a GeneChip Scanner 3000. Images are acquired and CEL files generated, which are then used for data analysis. Figure 1.3 shows an example of the fluorescence intensities on the chip recorded by an imaging device. 1.2. Experimental Design Based on our experience, the most critical factor in the experimental design of a DNA microarray experiment is to have a correct control for comparison, which means that we need to appropriately choose a cell or tissue source for isolating control RNA. Choosing a correct control can be extremely challenging, and sometimes it may not be possible to find a “perfect” control. For example, DNA microarray technology has been widely used in comparing gene expression profiling between tumor cells or tissues and corresponding normal cells or tissues in humans. Typically, total RNA is isolated from tumor cells/tissues that are heterogeneous in origin, and control RNA isolated from cells/tissues adjacent to the tumor cells/tissues or corresponding normal cells/tissues is also heterogeneous in most cases; cellular compositions of tumor and normal tissues are not the same or sometimes not even closely similar for appropriate comparisons. Although this cellular difference between tumor and normal tissues is a
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Fig. 1.3. DNA microarray analysis of gene expression profiles in pre-B leukemic cells expressing P190, P210, or P230 BCR-ABL. Total RNA was isolated from the parental ENU pre-B cell line and the same cell line expressing P190, P210, or P230. A total of 24 000 genes were measured using Affymetrix GeneChip, and the data were analyzed by F2 test. Expression of 2086 genes in BCR-ABL-expressing cells was found to be significantly different from that for the reference (parental cell control) (α < 0.05). The scientific background of the pre-B leukemic cells expressing P190, P210, or P230 BCRABL will be introduced in Chapter 2.
difficult technical problem in microarray studies, the current approach may have done its best to find the most appropriate controls available. Some of the findings obtained from this type of studies are real and useful, although some genes that play critical roles in tumor formation might not be detected due to the inappropriate controls used. A better way to improve this situation is to isolate subpopulations of tumor cells with antibodies recognizing phenotypical cell surface markers. This approach can be taken much more easily when studying human blood cancer cells. However, the
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availability and amount of human cells for sorting out a particular cell population can be difficult. In parallel, the different genetic background of each human patient will cause some differences in gene expression when a comparison is made among different patients with the same type of cancer. Regardless of these difficulties, DNA microarray technology has been found to be useful in the clinic, although there is still a long way to go before DNA microarray results can be used to support regulatory decision making or accurate and consistent prediction of patient outcomes. Here is an excellent example of how to perform a DNA microarray study using human cancer patient samples. In an elegant study of gene expression profiles of human breast cancer cells, a comparison of gene expression was made between breast cancer cells with higher tumorigenic capacity and normal breast epithelium (Liu et al., 2007). Specifically, the tumor cells were low or undetectable levels of CD24 (CD44+ CD24−/low), whereas the phenotype of cells from normal breast epithelium was unknown. A 186-gene invasion-associated signature obtained from this study suggests that there is a significant association between tumor invasion and both overall and metastasis-free survival in breast cancer patients. A critical question to ask is whether the normal breast epithelium is an appropriate control in this DNA microarray study. No matter what the answer is, it is clear that the normal breast epithelium is one of the best controls available. 1.3. Quality Control A major issue in DNA microarray technology is its repeatability and reproducibility. Repeatability refers to the ability to provide closely similar results from replicate samples processed in parallel at the same test location using the same gene expression assay. Reproducibility refers to the ability to provide closely similar results from replicate samples processed with different microarray platforms or at different test locations using the same gene expression assay. To achieve high repeatability and reproducibility, quality control has become a key issue in DNA microarray studies. A major criticism voiced about DNA microarray studies has been the lack of accuracy and reproducibility of the microarray data. The quality
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of DNA microarray results is associated with technical, instrumental, computational, and interpretative factors (Casciano and Woodcock, 2006). For the same samples tested, results obtained at different locations and between different microarray platforms could be different, making it difficult to use and interpret microarray data. Optimization and standardization of microarray procedures are critical steps. In this regard, the US Food and Drug Administration (FDA) has initiated a MicroArray Quality Control (MAQC) project among researchers in academic, government, and industrial institutions to seek to experimentally address the key issues surrounding the reliability of DNA microarray data. As part of this effort, since 2004 the FDA has started accepting voluntary genomic data submission with accompanying information related to the number and scope of DNA microarray-based expression data (Anonymous, 2006). The MAQC project aims to establish quality control metrics and thresholds for an objective assessment of the performance achievable by different microarray platforms, and for evaluation of the merits and limitations of various data analysis methods (Casciano and Woodcock, 2006). The specific concerns of the MAQC project relate to the impact of microarray data quality on genomic data submission (Frueh, 2006; Ji and Davis, 2006), the framework for the use of genomics data (Dix et al., 2006), comparisons of different commercial microarray platforms (Canales et al., 2006; Patterson et al., 2006; Shippy et al., 2006), the use of external RNA controls (Tong et al., 2006), interplatform and intraplatform reproducibility of gene expression measurements (Shi et al., 2006), and analytical consistency across microarray platforms (Guo et al., 2006). The main conclusion of the MAQC project is that, with careful experimental design and appropriate data transformation and analysis, microarray data can indeed be reproducible and comparable between different formats and laboratories, and that fold change results from microarray experiments correlate closely with results from well-accepted assays such as quantitative reverse transcription–polymerase chain reaction (qRT-PCR) (Anonymous, 2006). However, there is still a long way to go before we can answer the key remaining question: when can microarray data be used in a regulatory decision-making process?
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1.4. Interpretation of DNA Microarray Data While expensive, DNA microarray experiments have not yielded sufficient information that allows us to draw decisive conclusions. Often, what we have in the end is a long list of more than 30 000 genes with fold changes of their mRNA expression comparing control and experimental groups. An easy explanation for the fold change of mRNA expression of a particular gene, which is actually a way of obtaining information from DNA microarrray data by most people, is that mostly changed genes in expression are likely involved in the biological process being studied. A “hot” list is often provided by statisticians to researchers to describe which genes are mostly upregulated or downregulated, and those genes with minor or no changes in mRNA expression are often considered as not involved or not important by the researchers. However, there are numerous examples showing a disassociation between the abundance of mRNA and the level of translated protein for a gene of interest, and between the abundance of mRNA and the effect of the gene of interest on a particular biological process. From our own experience, a general guess on the biological role of a particular gene based on the fold change is sometimes agreeable to the results of experiments testing the function of this gene, but it is not rare to see misleading or wrong conclusions drawn based solely on the magnitude of the fold changes. Importantly, we learned that upregulation or downregulation of a gene does not necessarily mean a positive or negative role of this gene in the biological process being studied. A novel idea we intend to propose here is that a gene with no change in its expression, as determined by DNA microarray analysis, may still play a significant role in the biological process. In Chapter 2, we describe in much more detail and emphasize these ideas about the interpretation of DNA microarray data, with specific examples from our own studies. 1.5. Advantages and Disadvantages One of the biggest advantages of DNA microarray technology is that it can evaluate simultaneously the relative expression of thousands of genes by using small amounts of materials, providing gene signatures for particular
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disease situations. In addition, the procedures can easily be automated. Furthermore, the capacity of measurement of gene expression by DNA microarray is huge, allowing researchers to take the expression of all genes from an individual into consideration for disease analysis in socalled “personalized medicine”. One of the major disadvantages of DNA microarray technology is that it only evaluates gene expression at a transcriptional, but not translational, level, as posttranscriptional modifications (such as phosphorylation) often play significant roles in the regulation of protein functions. In addition, DNA microarray technology is still not mature enough for decision making based on the microarray data. In this book, we introduce our new way of interpreting and analyzing microarray data, which will hopefully bring us closer to success in decision making using the information obtained through DNA microarray technology (see Chapter 4).
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CHAPTER 2
Applications of DNA Microarray Technology in Cancer Research
Seven years ago, the sequencing of the human genome was completed (Lander et al., 2001; Venter et al., 2001), and many discussions and comments about the value of this information have been initiated since then. However, it is obvious that DNA microarray technology would not be possible without knowing the genome sequences of humans, mice, and other species as well. This technology has been widely used in basic and clinical studies of virtually all major human diseases, especially in cancer research. There are many good examples for studying gene expression in cancers, and here we only discuss some representative studies to show the applications of DNA microarray technology in cancer research. 2.1. Solid Tumors A DNA microarray study from Pantel’s group showed molecular signature associated with bone marrow micrometastasis in human breast cancer (Woelfle et al., 2003). In this study, gene expression profiles in metastasized breast tumor cells in bone marrow were compared with those in primary tumor cells, and expression analysis showed distinct profiles between these two groups of cells. The differentially expressed genes are related to extracellular matrix remodeling, adhesion, cytoskeleton plasticity, and signal transduction (in particular, the Ras and hypoxia-inducible factor 1α pathways). The array data were confirmed by RT-PCR, which is consistent with immunohistochemical analysis of breast tumor tissues. The findings from this study indicate that metastasized breast tumor cells 11
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exist as a selective process associated with a specific molecular signature. Study of the functional relevance of this molecular signature will shed light on the molecular diagnosis and therapy of human breast cancer. Another study published by Di Fiore’s group illustrated a beautiful experiment to show that survival of stage 1 lung adenocarcinomas could be predicted based on the expression of 10 genes discovered through analyzing the clinical DNA microarray data (Bianchi et al., 2007). The beauty of this study is that the original clues for identifying candidate genes came from their analyses of two pre-existing sets of clinical microarray data from two independent places (Michigan and Harvard, USA). The original datasets from Michigan included 86 adenocarcinomas of human lung cancer; those from Harvard included 84 cases. By analyzing these microarray data, a 80-gene model was created and tested on an independent cohort of lung cancer patients using RT-PCR. As a result, a 10-gene predictive model exhibited a prognostic accuracy of approximately 75% in stage 1 lung adenocarcinoma when tested on two additional independent cohorts. Potentially, this type of approach enables the discovery of similar predictive molecular signatures for other types of cancer. There are many more examples showing the application of DNA microarray technology in identifying molecular targets and establishing diagnostic molecular signatures for human cancers. The overall weakness of these microarray studies is the lack of ability to make decisions based solely on the DNA microarray data. 2.2. Blood Cancers An excellent example for using DNA microarray technology to study human blood cancers is the molecular classification of human acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) (Golub et al., 1999). In this study, bone marrow mononuclear cells from 11 AML and 27 ALL patients diagnosed pathologically were used as an RNA source for DNA microarray analysis, from which 50-gene predictors that distinguish AML from ALL were derived. These 50-gene predictors were tested and validated on 38 new samples from AML or ALL patients. As a result, 36 of the 38 predictions agreed with the patients’ clinical diagnosis (the remaining two were uncertain). This high prediction rate (95%)
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strongly suggests that DNA microarray technology may be used in the diagnosis of human blood cancers, although the improvement to a 100% prediction rate is the ultimate goal. 2.3. Our DNA Microarray Study Using Mouse Model of BCR-ABL-Induced Leukemia Here, we use our DNA microarray studies as examples to show our views on how a microarray experiment should be designed and how the data should be interpreted. To help explain the rationale of our study on leukemia cells and the mouse leukemia model we used, we first introduce some disease-related background information. Human Philadelphia chromosome-positive (Ph+) leukemias induced by the BCR-ABL oncogene are among the most common hematologic malignancies, and include chronic myeloid leukemia (CML) and B-cell ALL (B-ALL). The BCR gene, on chromosome 22, breaks at either exon 1, exon 12/13, or exon 19; and fuses to the c-ABL gene on chromosome 9 to form, respectively, three types of BCR-ABL: P190, P210, or P230. In humans, each of the three forms of the BCR-ABL oncogene is associated with a distinct type of leukemia [see review by Advani and Pendergast (2002)]. The P190 form is most often present in B-ALL, but only rarely in CML. P210 is the predominate form in CML, and in some acute lymphoid and myeloid leukemias in CML blast crisis. P230 was recently found in a very mild form of CML (Pane et al., 1996). CML has a triphasic clinical course: a chronic phase, in which BCR-ABL-expressing pluripotent stem cells massively expand but undergo normal differentiation to form mature neutrophils; an accelerated phase, in which neutrophil differentiation becomes progressively impaired and the cells become less sensitive to myelosuppressive medications; and blast crisis, a condition resembling acute leukemia in which myeloid or lymphoid blasts fail to differentiate. The transition from chronic phase to blast crisis results from additional genetic alterations, and this process is not well understood. Lymphoid blast crisis of CML and Ph+ B-ALL account for 20% of adults and 5% of children afflicted with ALL. Among those patients, 50% of adults and 20% of children carry P210BCR-ABL while the rest carry P190BCR-ABL (Druker et al., 2001a; Sawyers, 1999).
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The Abl tyrosine kinase inhibitor STI571 (imatinib mesylate; Gleevec) has become the most effective drug for leukemia therapy, and has been shown to induce a complete hematologic response in all interferon-resistant chronic-phase CML patients (Druker et al., 2001b). However, STI571 was unable to abrogate BCR-ABL-expressing leukemic cells (Marley et al., 2000) and induced cellular and clinical drug resistance (Branford et al., 2002; Gorre et al., 2001; le Coutre et al., 2000; Mahon et al., 2000; Shah et al., 2002; von Bubnoff et al., 2002; Weisberg and Griffin, 2000), suggesting that the use of STI571 as a single agent may not prevent eventual disease progression to terminal blast crisis or cure CML. Moreover, STI571 is much less effective in treating CML blast crisis patients (Druker et al., 2001a; Talpaz et al., 2000). Therefore, the development of new therapeutic drugs that are synergistic with available treatment strategies is critical. The success of this approach requires an understanding of the signaling pathways utilized by BCR-ABL to induce CML and B-ALL. Expression of BCR-ABL has been shown to activate multiple signaling molecules/ pathways, including Ras, MAPK, STAT, JNK/SAPK, PI-3 kinase, NF-κB, and c-MYC (Sawyers, 1997), as well as cytokine production (Anderson and Mladenovic, 1996; Hariharan et al., 1988). Recent studies also link BCRABL to apoptotic pathways (Amarante-Mendes et al., 1998; Dubrez et al., 1998; Goetz et al., 2001; Honda and Hirai, 2001; Jonuleit et al., 2000; Majewski et al., 1999; McGahon et al., 1995; Neshat et al., 2000; Parada et al., 2001; Sanchez-Garcia and Martin-Zanca, 1997; Skorski et al., 1997; Skorski et al., 1996) and to the activation of Src kinases in cultured cells (Danhauser-Riedl et al., 1996; Lionberger et al., 2000; Warmuth et al., 1997). Our studies in mice have shown the involvement of three Src family kinases — Lyn, Hck, and Fgr — in BCR-ABL-induced B-lymphoid leukemia (Hu et al., 2004). It is important to point out that, although these signaling molecules are present and activated in BCR-ABL-expressing cells, it remains unclear what role, if any, they play in the development of BCR-ABL-induced leukemias. The physiological relevance of the in vitro findings of the roles of signaling molecules in BCR-ABL signaling is better addressed in vivo using the mouse leukemia model, although in vitro studies (such as the use of cultured cells or cell lines) are often useful in finding preliminary clues.
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pMSCV-P210BCR/ABL-IRES-GFP LTR
P210 BCR/ABL
IRES
GFP
LTR
pMSCV-IRES-GFP LTR
IRES
GFP
LTR
(a)
(b)
Fig. 2.1. BCR-ABL induces both myeloid (CML) and lymphoid (B-ALL) diseases in mice. (a) The retroviral constructs used to transduce the P210BCR-ABL and GFP genes. (b) Wright/Giemsa staining of peripheral blood smears from mice with CML.
Although BCR-ABL induces both CML and B-ALL, here we only focus on the induction of CML by BCR-ABL. By retrovirally expressing the BCR-ABL oncogene in bone marrow cells, which are derived from donor BALB/c mice pretreated with 5-fluorouracil (5-FU), we are now able to produce CML in 100% of syngeneic recipients within 4 weeks (Li et al., 1999). The same CML can also be induced in C57BL/6 (B6) and other inbred strains. Mouse CML is characterized by leukocytosis with greatly elevated numbers of maturing neutrophils [Fig. 2.1(a)], organ infiltration, and splenomegaly. The target cells for BCR-ABL are primitive multipotential hematopoietic stem cells (Li et al., 1999), precisely mimicking most of the pathological characteristics of human CML. Myeloid cells transduced by the BCR-ABL oncogene express BCR-ABL protein (Li et al., 1999). The control retroviral vector containing only the GFP gene [Fig. 2.1(b)] has never induced CML in recipient mice within 12 months (data not shown). We carried out a microarray study to identify genes that are regulated by BCR-ABL in cell lines and in our mouse CML model. 2.3.1. Leukemia mouse model study 2.3.1.1. Rationale The Abl tyrosine kinase inhibitor imatinib has become the most effective drug for leukemia therapy, and has been shown to induce a complete hematologic response in all interferon-resistant chronic-phase CML patients
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(Druker et al., 2001b). However, imatinib was unable to abrogate BCRABL-expressing leukemic cells (Marley et al., 2000) and induced cellular and clinical drug resistance (Branford et al., 2002; Gorre et al., 2001; le Coutre et al., 2000; Mahon et al., 2000; Shah et al., 2002; von Bubnoff et al., 2002; Weisberg and Griffin, 2000), suggesting that the use of imatinib as a single agent may not prevent eventual disease progression to terminal blast crisis or cure CML. Moreover, imatinib is much less effective in treating CML blast crisis patients (Druker et al., 2001a; Talpaz et al., 2000). Recently, a newly developed Abl kinase inhibitor (termed BMS-354825 and produced by Bristol-Myers Squibb) has been shown to have an inhibitory effect on almost all imatinib-resistant BCR-ABL mutants (Shah et al., 2004), offering some hope for overcoming imatinib resistance. However, the BCR-ABL-T315I mutant that frequently appears in patients resistant to imatinib therapy is still resistant to BMS-354825 (Shah et al., 2004). Another novel Abl kinase inhibitor, named AP23464, has also been effective against several frequently observed imatinib-resistant BCR-ABL mutants, but ineffective against the BCR-ABL-T315I mutant (O’Hare et al., 2004). While clinical trials are ongoing to determine the effectiveness of these drugs in treating human Ph+ leukemia patients who are resistant to imatinib, our preliminary results have shown that imatinib does not eradicate leukemic cells in mice. Therefore, it is critical to develop new therapies that are synergistic with available treatment strategies and capable of killing BCR-ABLexpressing leukemic stem cells (LSCs). The success of this approach requires an understanding of the signaling pathways utilized by BCR-ABL in LSCs to induce Ph+ leukemias. Molecular pathways involved in regulation of the self-renewal and survival of Ph+ LSCs are largely unknown. We have identified BCR-ABL-expressing hematopoietic stem cells (HSCs) (GFP+Lin−cKit+Sca-1+) as CML stem cells (Hu et al., 2006), providing a valuable assay system for studying the biology of LSCs. Using this LSC assay system, we will be able to identify and test molecular targets in LSCs. 2.3.1.2. Experimental design Bone marrow cells are isolated from the long bones of CML mice that are untreated or treated with imatinib. BCR-ABL-expressing or nonBCR-ABL-expressing (transduced with the empty GFP vector) HSCs (GFP+Lin−c-Kit+Sca-1+) are stored by fluorescence-activated cell sorting
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(FACS) directly into RNAlater (Ambion, Austin, TX, USA) and are homogenized in RLT Buffer (RNeasy Micro Kit; Qiagen, Valencia, CA, USA). Total RNA is isolated by following the protocol for the RNeasy Micro Kit, and quality is assessed using a 2100 Bioanalyzer instrument and RNA 6000 Pico LabChip assay (Agilent Technologies, Palo Alto, CA, USA). Utilizing the GeneChip Whole Transcript Sense Target Labeling Assay kit (Affymetrix, Santa Clara, CA, USA), 100–300 ng of total RNA undergoes reverse transcription with random hexamers tagged with T7 sequence. The double-stranded cDNA that is generated is then amplified by T7 RNA polymerase to produce cRNA. Secondcycle first-strand cDNA synthesis then takes place, incorporating dUTP, which is later used as sites where fragmentation occurs by utilizing a uracil DNA glycosylase and apurinic/apyrimidinic endonuclease 1 enzyme mix. The fragmented cDNA is then labeled by terminal transferase, attaching a biotin molecule using Affymetrix proprietary DNA Labeling Reagent. Approximately 2.0 µg of fragmented and biotin-labeled cDNA is then hybridized onto a Mouse Gene ST 1.0 Array (Affymetrix, Santa Clara, CA, USA) for 16 hours at 45°C. Posthybridization staining and washing are performed according to the manufacturer’s protocols using the Fluidics Station 450 instrument (Affymetrix). Finally, the arrays are scanned with a GeneChip Scanner 3000. Images are acquired and CEL files generated, which are then used for analysis. 2.3.1.3. Results The microarray data — shown as upregulation, downregulation, or no change — of more than 30000 mouse genes in the presence and absence of BCR-ABL in LSCs have been sent to the National Center for Biotechnology Information (NCBI) through Gene Expression Omnibus (GEO) submission. In sum, several gene expression patterns are observed when considering how the expression of a particular gene is changed before and after the treatment with the BCR-ABL kinase inhibitor imatinib (Table 2.1): • Expression of a gene is upregulated by BCR-ABL as compared to the control, and imatinib treatment reduces the expression level of the gene to the control level (Pattern A).
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Table 2.1. Gene expression patterns of BCR-ABL-expressing cells treated with or without imatinib (IM). Gene expression levels in experimental groups Expression pattern A B C D E F G H I
Control
BCR-ABL
BCR-ABL + IM
Explanation
+++ +++ +++ +++ +++ +++ +++ +++ +++
+++++ + +++++ + +++++ ++ +++ +++ +++
+++ +++ +++++ or ++++ + or ++ +++++++ + + +++++ +++
positively involved negatively involved positively involved negatively involved negatively involved positively involved positively involved negatively involved not involved
• Expression of a gene is downregulated by BCR-ABL as compared to the control, and imatinib treatment increases the expression level of the gene toward or to the control level (Pattern B). • Expression of a gene is upregulated by BCR-ABL as compared to the control, and imatinib treatment does not reduce the expression level of the gene toward or to the control level (Pattern C). • Expression of a gene is downregulated by BCR-ABL as compared to the control, and imatinib treatment does not increase the expression level of the gene toward or to the control level (Pattern D). • Expression of a gene is upregulated by BCR-ABL as compared to the control, and imatinib treatment further increases the expression level of the gene (Pattern E). • Expression of a gene is downregulated by BCR-ABL as compared to the control, and imatinib treatment further reduces the expression level of the gene (Pattern F). • Expression of a gene is not changed by BCR-ABL as compared to the control, and imatinib treatment reduces the expression level of the gene (Pattern G).
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• Expression of a gene is not changed by BCR-ABL as compared to the control, and imatinib treatment increases the expression level of the gene (Pattern H). • Expression of a gene is not changed by BCR-ABL as compared to the control, and the expression level of the gene remains the same upon imatinib treatment (Pattern I). Detailed explanations for these patterns are described in Table 2.1. In summary, these patterns have changed our way of interpreting DNA microarray data. What we learned from this study is that, in oncogenestimulated cell proliferation, an upregulated gene is not always positively involved in cancer development, a downregulated gene is not always negatively involved in cancer development, and, importantly, a gene with no change in expression may also be critically involved in cancer development. Thus, the expression level of a gene is relative and does not always reflect its function. It could be misleading to determine the function of a gene based on the expression level of the gene. 2.3.2. Cell line study The abovementioned explanations of our microarray data are largely different from those most people would have. We tested these ideas by performing an independent microarray experiment in a totally different assay system. We studied gene expression profiling in BCR-ABLexpressing lymphoid cells before and after treatment with imatinib. We first made a B-lymphoid cell line whose growth does not depend on BCRABL, and the control cell line is the parental line that does not express BCR-ABL (Hu et al., 2004). Our results also show the gene expression patterns A–J described above, allowing us to reach the same conclusions that an upregulation, downregulation, or no change of expression of a gene does not necessarily reflect the role of this gene in tumor cell growth. A clear conclusion can be drawn by having a critical control that represents a situation when the function of the oncogene of interest is inhibited genetically or by an inhibitory agent.
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CHAPTER 3
Current Analytical Methods of DNA Microarray Data
Analysis of DNA microarray data includes multiple steps. Here, we describe some general procedures for the analysis, using our microarray data discussed in Table 3.1 as an example. 3.1. Experimental Design This experiment had one factor, Strain, a fixed factor with three levels (B6, Mut A, and Vector with 3, 2, and 2 biological replicates, respectively). A total of eight Affymetrix Mouse Gene 1.0 ST Arrays were used for gene expression analysis (Table 3.1). 3.2. Method 3.2.1. Robust multi-chip averaging (RMA) Average signal intensities for each probe set within arrays were calculated by and exported from Affymetrix’s Expression Console (Version 1.1) software using the RMA method, which incorporates convolution background correction and a summarization based on a multi-array model fit robustly using the median polish algorithm. These values were read into R/maanova and quantile-normalized. For this experiment, three pairwise comparisons will be used to statistically resolve gene expression differences between strain groups using the R/maanova analysis package (Wu et al., 2003). Specifically, differentially expressed genes will be detected by using Fs, a modified F-statistic incorporating shrinkage estimates of 21
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Table 3.1. Experimental design. Note that Mut_A_1 or Mut_A_2, B6_1 or B6_2, and Vector_1 or Vector_2 are names of the RNA samples for DNA microarray experiment. Array GC_Gene1.0ST_GES08_0064_040208_1 GC_Gene1.0ST_GES08_0065_040208_1 GC_Gene1.0ST_GES08_0066_040208_1 GC_Gene1.0ST_GES08_0067_040208_1 GC_Gene1.0ST_GES08_0068_040208_1 GC_Gene1.0ST_GES08_0069_040208_1 GC_Gene1.0ST_GES08_0070_040208_1 GC_Gene1.0ST_GES08_0071_040208_1
Strain
Strain_Rep
Sample
Dye
Mut_A B6 Vector B6 Mut_A B6 Vector B6_P210
Mut_A_1 B6_1 Vector_1 B6_2 Mut_A_2 B6_3 Vector_2 B6_P210_1
1 2 3 4 5 6 7 8
1 1 1 1 1 1 1 1
variance components from within the R/maanova package (Cui et al., 2005; Wu et al., 2003). Statistical significance levels of the pairwise comparison will be calculated by permutation analysis (1000 permutations) and adjusted for multiple testing using the false discovery rate (FDR), q-value, method (Storey, 2002). Differentially expressed genes are declared at an FDR q-value threshold of 0.05. 3.2.2. iterPLIER Average signal intensities for each probe set within arrays were calculated by and exported from Affymetrix’s Expression Console (Version 1.1) software using the iterPLIER method with background adjustment (PMGCBG). These values were read into R/maanova; a value of 16 was added to the processed probeset intensities to account for iterPLIER’s zerobased method, log-base-2-transformed, and quantile-normalized to equalize the distribution of intensities across all arrays. For this experiment, three pairwise comparisons will be used to statistically resolve gene expression differences between strain groups using the R/maanova analysis package (Wu et al., 2003). Specifically, differentially expressed genes will be detected by using Fs, a modified F-statistic incorporating shrinkage estimates of variance components from within the R/maanova package (Cui et al., 2005; Wu et al., 2003). Statistical significance levels
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Table 3.2. Individual probes with an intensity reading > 60000). exceeding that of the saturation of the scanner (> ID
Probe
Row
Intensity
GC_Gene1.0ST_GES08_0065_040208_1 10387588 10599607
12 32
165140 785261
65533 65534
Fig. 3.1. Boxplots of raw (top) and log2-transformed (bottom) intensities for all probes on all arrays.
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Fig. 3.2. samples.
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Boxplot of log2-transformed, RMA-processed probe intensities for all
Fig. 3.3. Boxplot of log2-transformed, iterPLIER-processed probe intensities for all samples.
of the pairwise comparison will be calculated by permutation analysis (1000 permutations) and adjusted for multiple testing using the false discovery rate (FDR), q-value, method (Storey, 2002). Differentially expressed genes are declared at an FDR q-value threshold of 0.05.
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Fig. 3.4. Boxplot of log2-transformed, quantile-normalized, RMA-processed signal intensities for all samples.
Fig. 3.5. Boxplot of log2-transformed, quantile-normalized, iterPLIER-processed signal intensities for all samples.
3.3. Quality Control Diagnostics 3.3.1. Saturation Raw intensities for each sample were examined to determine whether any probes had a signal close to the saturation of the scanner
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Fig. 3.6. Scatterplots of RMA-processed, normalized intensities for each sample against all other samples.
(>60 000). The listed probes had measurements with an intensity reading greater than 60 000. The number of probes for all arrays was 6 552 328. The range of intensities was from 22 to 65 534 (Table 3.2).
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Fig. 3.7. Scatterplots of iterPLIER-processed, normalized intensities for each sample against all other samples.
3.3.1.1. Raw intensities for arrays containing a saturated probe A boxplot of raw and log2-transformed intensities for all probes for arrays containing a probe with an intensity reading >60 000 reveals whether a probe is an outlier or if an array has consistently high readings (Fig. 3.1).
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Fig. 3.8. Average MA plot of RMA-processed data for sample groups Mut_A versus B6 (top left), Mut_A versus Vector (top right), and Vector versus B6 (bottom center).
3.3.2. Transformed intensities across arrays For each probe set, the processed raw intensities for all probes were averaged and log2-transformed without normalization from Affymetrix’s Expression Console software. The log2-transformation of raw data was used to reduce the impact of outliers and ensure normality (Figs. 3.2 and 3.3).
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Fig. 3.9. Average MA plot of iterPLIER-processed data for sample groups Mut_A versus B6 (top left), Mut_A versus Vector (top right), and Vector versus B6 (bottom center).
3.3.3. Normalized intensities across arrays The log2-transformed intensities were quantile-normalized. Quantilenormalization normalizes data based on the magnitude of the measures of probe intensities. The method is employed to remove systematic effects, and to bring the data from different microarrays onto a common scale (Figs. 3.4 and 3.5).
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Fig. 3.10. Histogram of the distribution of Fs permutation p-values resulting from the contrast of RMA-processed data for sample groups Mut_A versus B6 (top left), Mut_A versus Vector (top right), and Vector versus B6 (bottom center).
3.3.4. Scatterplot of normalized intensities A scatterplot of log2-transformed, quantile-normalized intensities allows for the comparison of the degree of similarity (i.e. correlation) of samples within a group to the degree of similarity of samples between groups. Arrays are plotted in groups according to sample type (Figs. 3.6 and 3.7).
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Fig. 3.11. Histogram of the distribution of Fs permutation p-values resulting from the contrast of iterPLIER-processed data for sample groups Mut_A versus B6 (top left), Mut_A versus Vector (top right), and Vector versus B6 (bottom center).
3.3.5. Average MA plot of normalized intensities MA plots are used to visualize the similarity of groups of samples. The x-axis represents the average log intensity of one factor level versus another, whereas the y-axis represents the log ratio (Figs. 3.8 and 3.9).
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DNA Microarray Technology and Data Analysis in Cancer Research Table 3.3.
Cumulative number of significant calls using RMA-processed data.
